基于rnn的旅游景点序列感知推荐

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hee Jun Lee, Yang Sok Kim, Won Seok Lee, In Hyeok Choi, Choong Kwon Lee
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引用次数: 0

摘要

实时选择合适的旅游景点是旅游者面临的一个重要问题。由于推荐器会根据用户偏好主动推荐商品,因此它们是解决这个问题的一个很有希望的解决方案。旅游者通过同时考虑多个属性来顺序访问旅游景点。因此,在制定旅游景点推荐时,最好考虑到这一点。使用GRU4REC,我们提出了基于rnn的序列感知推荐器(rnn - sar),它使用多个序列数据集来训练推荐模型,称为multi- rnn - sar。我们提出了两种类型的多rnn - sars -串联rnn - sars和并行rnn - sars。为了评估多RNN-SAR,我们比较了基于项目的协同过滤推荐器(item-CFR)、RNN-SAR与单序列数据集(basic-RNN-SAR)、多RNN-SAR和使用真实旅行数据集的最先进sar的命中率(HR)和平均倒数等级(MRR)。我们的研究表明,与单项cfr相比,多重rnn - sar具有显著更高的性能。并非所有的多rnn - sar都优于基本rnn - sar,但最好的多rnn - sar可以达到与最先进算法相当的性能。这些结果强调了在rnn - sar中使用多个序列数据集的重要性,以及在实践中选择合适的序列数据集和学习方法来实现多rnn - sar的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

RNN-Based Sequence-Aware Recommenders for Tourist Attractions

RNN-Based Sequence-Aware Recommenders for Tourist Attractions

RNN-Based Sequence-Aware Recommenders for Tourist Attractions

RNN-Based Sequence-Aware Recommenders for Tourist Attractions

Selecting appropriate tourist attractions to visit in real time is an important problem for travellers. Since recommenders proactively suggest items based on user preference, they are a promising solution for this problem. Travellers visit tourist attractions sequentially by considering multiple attributes at the same time. Therefore, it is desirable to consider this when developing recommenders for tourist attractions. Using GRU4REC, we proposed RNN-based sequence-aware recommenders (RNN-SARs) that use multiple sequence datasets for training the recommended model, named multi-RNN-SARs. We proposed two types of multi-RNN-SARs—concatenate-RNN-SARs and parallel-RNN-SARs. In order to evaluate multi-RNN-SARs, we compared hit rate (HR) and mean reciprocal rank (MRR) of the item-based collaborative filtering recommender (item-CFR), RNN-SAR with the single-sequence dataset (basic-RNN-SAR), multi-RNN-SARs and the state-of-the-art SARs using a real-world travel dataset. Our research shows that multi-RNN-SARs have significantly higher performances compared to item-CFR. Not all multi-RNN-SARs outperform basic-RNN-SAR but the best multi-RNN-SAR achieves comparable performance to that of the state-of-the-art algorithms. These results highlight the importance of using multiple sequence datasets in RNN-SARs and the importance of choosing appropriate sequence datasets and learning methods for implementing multi-RNN-SARs in practice.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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